AI 中文总结
该研究提出贝叶斯融合森林框架，结合随机试验与真实世界数据估计生存异质性处理效应，经模拟和HIV数据分析显示其效率优于仅试验分析，可明确识别患者获益。
AI 中文摘要
我们提出贝叶斯融合森林这一非参数框架，用于结合随机对照试验与真实世界数据，估计生存结局的异质性处理效应。该框架通过假设处理效应可在两类数据源间迁移，放松了真实世界数据的无混杂假设。我们的方法将右删失和区间删失结局纳入数据融合范畴，采用加速失效时间分解建模生存时间，分解为共享基线预后、源特异性偏差、处理效应和混杂函数，其中混杂函数吸收真实世界数据中的混杂偏差，各组件均采用贝叶斯树集成先验；共享基线预后跨源借用强度，偏差捕捉源间异质性，分层狄利克雷过程混合非参数建模误差分布。模拟研究显示，在不同混杂程度和源间异质性水平下，该方法相比仅试验分析具有效率提升。我们结合ACTG 175试验与多中心艾滋病队列研究，估计HIV联合抗逆转录病毒疗法的效应，融合分析识别出几乎所有患者均获益，而单独试验分析结论不明确。
英文摘要
We develop the Bayesian fusion forest, a nonparametric framework to estimate heterogeneous treatment effects on survival outcomes by combining a randomised controlled trial and real-world data. The framework relaxes the unconfoundedness assumption on the real-world data by assuming instead that the treatment effect transports across the two sources. Our method opens up right- and interval-censored outcomes to data fusion. We model the survival time with an accelerated failure time decomposition into a shared baseline prognosis, a source-specific deviation, a treatment effect, and a confounding function. The confounding function absorbs the confounding bias in the real-world data. Each component receives a Bayesian tree ensemble prior. The shared baseline prognosis borrows strength across sources, while the deviation captures between-source heterogeneity. A hierarchical Dirichlet process mixture models the error distribution nonparametrically. A simulation study shows efficiency gains over a trial-only analysis across varying levels of confounding and between-source heterogeneity. We combine the ACTG 175 trial with the Multicenter AIDS Cohort Study to estimate the effect of combination antiretroviral therapy for HIV. The fusion identifies a benefit for nearly every patient whereas the trial alone is inconclusive.
